In this paper we investigate to what extent firm investment in transition countries is sensitive to internal finance. We use accounts data of over 4000 companies in four countries at different stages of transition. We find that firms in Bulgaria and Romania are less sensitive to internal financing constraints, in contrast to firms in Poland and the Czech Republic. A likely explanation is that Bulgaria and Romania, which are the least advanced in the reforms towards market economy, have a stronger persistence of soft budget constraints than in the other two more advanced countries.Investment; Trade; Credit; Economy; Companies;

Konings, Joep

Rizov, M

Vandenbussche, Hylke

English

Research Papers in Economics

KATHOLIEKE 
UNlVERSlTEIT 
LEUVEN 
DEPARTEMENT TOEGEPASTE 
ECONOMISCHE WETENSCHAPPEN 
RESEARCH  REPORT 0204 
INVESTMENT AND CREDIT CONSTRAINTS IN 
TRANSITION ECONOMIES: MICRO EVIDENCE 
FROM  POLAND, THE CHECH REPUBLIC, 
BULGARIA AND ROMANIA 
by 
J. KONINGS 
M. RIZOV 
H.VANDENBUSSCHE 
0/2002/2376/04 INVESTMENT AND CREDIT CONSTRAINTS IN TRANSITION ECONOMIES: 
MICRO EVIDENCE FROM POLAND, THE CZECH REPUBLIC, BULGARIA AND 
ROMANIA 
JozefKonings*, Marian Rizov** and Hylke Vandenbussche*** 
*LICOS, Centre for Transition Economics 
Economics Department 
Katholieke Universiteit Leuven 
Deberiotstraat 34 
3000 Leuven 
Belgium 
And 
CEPR, London 
** LICOS, Centre for Transition Economics 
Economics Department 
Katholieke Universiteit Leuven 
Deberiotstraat 34 
3000 Leuven 
Belgium 
*** Department of Applied Economics 
Katholieke Universiteit Leuven 
N  aamsestraat 69 
3000 Leuven 
Belgium 
And 
CEPR, London 
January 2002 
Abstract 
In this paper we investigate to what extent firm investment in transition countries is sensitive 
to  internal  finance.  We use  accounts  data  of over 4000  companies  in  four  countries  at 
different stages of transition. We find that firms in Bulgaria and Romania are less sensitive to 
internal financing constraints, in contrast to firms in Poland and the Czech Republic.  A likely 
explanation is that Bulgaria and Romania, which are the least advanced in the reforms towards 
market economy, have a stronger persistence of soft budget constraints than in the other two 
more advanced countries. 
Keywords: Investment, financial constraints, soft budget constraint, transition to a market 
economy 
JEL classification codes: E22, G32, P21, D2l, D92 
We are grateful to the Belgian Fund for Scientific Research (FWO) for providing financial 
support. I.  Introduction 
The transition from a centrally planned to a market orientated economy that started in 
Central  and  Eastern  Europe  more  than  a  decade  ago  implied  the  need  for  substantial 
restructuring of many inefficient firms.  Theoretical models made a distinction between initial 
and strategic restructuring (e.g. Grosfeld and Roland,  1997; Aghion, Blanchard and Burgess, 
1994; Blanchard, 1997).  While the former is concerned with getting rid of labor hoarding, the 
latter is concerned with achieving long run viability of firms. 
One of the  key  components  of strategic  restructuring in  these  models  refers  to  the 
investment decision that firms  need to make.  At  the start of transition it became clear that 
most firms had to invest heavily to modernize their obsolete capital stock and thus to improve 
their  long run  viability  if they  were  to  compete  in  international  markets.  However,  the 
external conditions such as the functioning of capital markets and the presence or absence of 
soft budget constraints are of crucial importance to achieve such  strategic restructuring.  In 
particular, if soft budget constraints (SBC) persist (i.e., the government or other institutions 
bailout loss  making  firms),  restructuring  may  be  postponed  (Kornai,  1999;  Dewatripont, 
Maskin and Roland, 2000).  In their 1999 transition report, the EBRD has indeed documented 
the continuation of soft budget constraints in  most of the transition economies, not through 
direct subsidies, but rather via indirect ways, such as tax arrears or "preferential" bank loans. 
In this paper we use a unique panel of more than 4,000 manufacturing firms consisting 
of comparable data for  Poland,  the Czech Republic,  Bulgaria and Romania between  1994-
1999.  The  main  purpose  is  to  contrast  the  investment behavior  of firms  in  the  slowly 
reforming  economies  of Bulgaria  and  Romania  versus  those  in  fast  reforming  countries, 
Poland and the Czech Republic.  We find firms in Poland and the Czech RepUblic to be credit 
constrained, suggesting that capital markets are not functioning properly.  In contrast firms in Bulgaria and Romania seem far less dependent on internal financing to invest.  We interpret 
this  result  as  evidence  of stronger  persistence  of SBC  in  this  group  of  slowly reforming 
transition countries.  The remainder of the paper is organized as follows.  Section II introduces 
the  methodology  and  discusses  estimation  issues.  Section ill describes  the  data  set  and 
discusses the estimation results.  The final section IV concludes. 
II. Background, Empirical Model and Estimation Issues 
The  standard  accelerator  model  of investment,  assuming  perfect  capital  markets, 
relates  investment to  the output performance in firms.l  Financial variables  should have no 
impact  on  the  investment  decision  of firms  as  internal  and  external  finance  are  perfect 
substitutes in perfect capital markets. However, in the context of transition economies where 
capital markets are just emerging and in the face of substantial information asymmetries, the 
assumption of perfect capital markets is harder to defend. 
There  are  only  a handful  of papers  that  study  the  investment behavior of firms  in 
transition  countries.  Lizal  and  Svejnar  (2001)  and  Anderson  and  Kegels  (1997)  analyze 
investment in firms in the Czech Republic in the 1990s, while Budina, Garretsen and de Jong 
(2000) analyze investment of Bulgarian firms over the period 1993-1995. 
The  empirical  equation  that  we  seek  to  estimate  has  the  following  standard 
specification: 
r't  Qit  CFit 
~+al--+a2~at  , 
Kit-l  Kit-l  Kit-l 
(1) 
2 where  I  stands  for  gross  investment  defined  as  the  change  in  the  real  capital  stock plus 
depreciation, Q is the growth of real sales of the firm, CF is the real cash flow of the firm, K is 
the level of the real capital stock (proxied by tangible fixed assets), subscript i refers to firm i 
and subscript t refers to year. We normalize by the capital stock to control for size effects. We 
deflated all nominal values with the producer price index.  The parameter aj represents  an 
unobservable  firm  level  fixed  effect  that  may  be  correlated  with  the  other  explanatory 
variables and Sit is a white noise error term. 
As in other studies, the growth in real sales in equation (1) proxies for the investment 
opportunities. The coefficient a2 captures the sensitivity of firm level investment with respect 
to the internal financing of the firm and is the coefficient of interest.  A low value of the cash 
flow coefficient, a2 , suggests that the firm has access to external finance while a high value of 
a2  suggests that investment is  largely dependent on the profitability and the liquidity of the 
firm as measured by the cash flow? 
To control for the unobserved firm level fixed effect we will estimate equation (1) in 
first differences or 
Iii  Qit  CFiI 
~-v:-:: = al~--+  a2~--+  ~SiI . 
1'>.it-\  KiI-l  Kit-l 
(2) 
By estimating equation (2) we can obtain consistent estimates of the parameters of interest and 
at  the  same  time  control  for  potential  firm  heterogeneity.  Furthermore,  we  will  treat  the 
regressors as endogenous as it is conceivable that higher investment leads to higher changes in 
sales and higher cash flows. There may be measurement error in the variables as well. We will 
1  Some of the most successful empirical investment models are  based on the traditional acceleration principle, 
which  links  the  demand  for  capital  goods  to  the  level  or  change  in  fIrm's  output or sales.  (e.g.,  Abel  and 
Blanchard, 1986 and Fazzari, Hubbard and Petersen, 1988). 
3 estimate equation (2)  including year dummies to  control for unobserved common aggregate 
shocks. 
Arellano and Bond (1991) demonstrate that in  a first difference model as  in equation 
(2) good instruments are the values of the endogenous explanatory variables dated (-2 and at 
earlier dates as they are not correlated with the contemporaneous first differenced error term. 
As the panel progresses an increasing number of instruments can be used which increases the 
efficiency  of the  estimates.  We  will  therefore  compute  a  Sargan  test  of overidentifying 
restrictions  to test the validity of the instruments  used.  Furthermore, we  will  also  report a 
second order serial correlation test (SOC)  which is  asymptotically  N(O,I) distributed.  The 
absence of second order serial correlation is  consistent with the absence of first order serial 
correlation in the levels equation (1). This is important to verify as we are using lagged values 
of the endogenous explanatory variables as instruments. 
Ill.  Data and Results 
We use data from  company accounts  recorded in  the AMADEUS database.3  To be 
included in AMADEUS companies must comply with at least one of the following criteria: (i) 
turnover greater than  10 million EUR; (ii) number of employees greater than  150;  (iii) total 
assets greater than 10 million EUR.  Uniformity is achieved by standardization of accounting 
information enabling easy cross border analysis.  We examine the time period  1994-1999, a 
period for which AMADEUS has a large coverage of firms.  We trace firms for at least three 
2 Experimenting with other proxies for the liquidity variable, such as the liquidity ratio yielded qualitatively the 
same results. 
3  AMADEUS  is a Pan-European financial  database,  created and  distributed by the Bureau Van  Dijk on CD-
ROM, containing information on medium and  large public and  private companies.  The data are collected by 
local information providers and Bureau Van Dijk makes them consistent to faciliate cross-country comparisons. 
For Bulgaria are provided by Creditreform Bulgaria 000; for Czech Republic-by Albertina Data; for Poland-by 
InfoCredit; and for Romania-by Romanian Chamber of Industry and Commerce. 
4 consecutive years and table 1 gives an overview of the structure of the panel and the number 
of firms in each country. 
Summary statistics of the relevant variables such as  employment, investment, growth 
of sales and cash flow are reported in table 2.  We can already note that firms in Bulgaria and 
Romania, the two slow reformers, have on average lower investment rates and lower growth 
rates in real sales. 
The regression results  are  reported in table 3.  The results  are  IV GMM estimates 
where we treat the growth in real sales and the cash flow as endogenous. In the first column 
we  report  the  unconstrained  model,  while  in  the  second  column  we  report  the  liquidity 
constrained model.  The estimated coefficient a], of the change in  real sales controlling for 
investment opportunities, is positive in all countries and similar in magnitude to what has been 
reported in previous studies for transition economies.  This suggests that the accelerator model 
is not a bad approximation for describing investment behavior in transition economies. 
We next tum to the discussion of liquidity constraints in column (2).  The coefficients 
on the cash flow  take different values for the different countries in our sample.  While for 
Poland and the Czech Republic,  a2 is  highly significant and positive this is  not the case for 
Bulgaria and Romania.  For Bulgarian firms we find a relatively low coefficient of 0.025 that 
is statistically significant only at the 10% critical level.  This compares to a coefficient of 0.07 
for both Polish and Czech firms.  For Romanian firms there is no statistically significant effect 
of cash flows on investment.  Thus firms in Poland and the Czech Republic seem to be credit 
constrained,  a  result  often  observed  in  western  market  economies  as  well.  To  put  it 
differently,  firm  investment  in  these  countries  seems  to  depend  on the  liquidity  of firms. 
Furthermore, credit constraints seem far less present in the least advanced transition countries, 
Bulgaria and Romania. It is unlikely that this is  a reflection of perfect capital markets, given 
the  many  uncertainties  and  the  early  stage  of financial  reforms  characterizing  these  two 
5 countries.  The alternative explanation is that soft budget constraints prevail in Bulgaria and 
Romania, which implies that firms are not operating under liquidity constraints.  When access 
to  credit is  facilitated  through  preferential  lending  under  a  variety  of patterns,  investment 
becomes less sensitive to internal firm financing. 
Finally, as  a robustness check of the sensitivity of our results to outliers, we have re-
estimated  every  country  regression  for  a  sample  excluding  firm-years  with  negative 
investment to capital ratios, which can be considered as outliers in the dataset.  The results are 
presented in table 4 and show to be qualitatively the same as the results for the full sample. 
IV.  Conclusion 
This paper uses  comparable firm  level  data for  four  transition  economies, two  fast 
reformers and two slow reformers, to analyze the impact of internal financial constraints on 
firm's  investment behavior.  For the fast  reformers  we  find,  as  in  well-developed market 
economies, that firms are liquidity constrained in their investment decisions.  However, in the 
slowly reforming economies, we find that such constraints are less important.  This is unlikely 
a reflection of the presence of perfect capital markets, rather it is likely to be a reflection of the 
presence of soft budget constraints in these countries. 
A further exploration of how soft budget constraints influence firm's restructuring and 
performance is not only important in transition economies, but also in well-developed market 
economies, where soft budget constraints may exist. 
6 References 
Abel,  A.  and  O.  Blanchard (1986)  "The present value of profits  and cyclical movement in 
investment." Econometrica, 54(2): 249-73. 
Aghion,  A.,  O.  Blanchard and R.  Burgess  (1994)  "The behavior  of state firms  in  Eastern 
Europe pre-privatization." European Economic Review, 38(6): 132-49. 
Anderson, R. and Ch. Kegels (1997) "Finance and investment in transition: Czech enterprises, 
1993-1994."  IRES-Institut  de  Recherces,  Department  of  Economics,  Universite 
Cetholique de Lovain, Discussion Paper 9715, Louvain-Ia-Neuve. 
Arellano, M.  and S. Bond (1991)  "Some tests of specification for panel data:  Monte Carlo 
evidence and an application to employment equations." Review of Economic Studies, 
58: 277-97. 
Blanchard, o. (1997) The Economics of  Post-Communist Transition, Clarendon Press: Oxford. 
Budina,  N.,  H.  Garretsen,  and E.  de  Jong  (2000)  "Liquidity constraints  and investment in 
transition economies: the case of Bulgaria." Economics of  Transition, 8: 453-75. 
Dewatripont, M., Maskin, E. and Roland, G.  (2000). "Soft Budget Constraints and Transition" 
in eds. Eric Maskin and Adras Simonovits, Planning,  Shortage,  and Transformation: 
Essays in Honor of  Janos Kornai, MIT press. 
Fazzari,  S.,  G.  Hubbard,  and  B.  Petersen  (1988)  "Financing  constraints  and  corporate 
investment." Brookings Papers on Economic Activity, 19: 141-95. 
Grosfeld, I. and G. Roland (1997) "Defensive and strategic restructuring in Central European 
enterprises." Journal of  Transforming Economies and SOcieties, 3(4): 21-46. 
Kornai,  J.  (1999).  "Hardening  the  Budget  Constraint:  The  Experience  of Post-Socialist 
Countries", Collegium Budapest Discussion Paper Series 59. 
Lizal, L. and J.  Svejnar (2001)  "Investment, credit rationing and the soft budget constraint: 
Evidence from Czech panel data." Review of  Economics and Statistics, forthcoming. 
7 Table 1 Structure of  the panel 
Years of  Number of firms 
observation  Poland  Czech Republic  Bulgaria  Romania 
3  251  221  268  196 
4  148  379  293  551 
5  55  444  621  983 
Total number of 
firms  454  1044  1182  1730 Table 2 Summary statistics 
Poland  Czech ReEublic  Bulgaria  Romania 
Mean  St.Dev.  Mean  St.Dev.  Mean  St.Dev.  Mean  St.Dev 
E  514  978  504  1034  386  761  743  1391 
UK  0.049  0.186  0.132  0.290  0.017  0.475  -0.108  0.46 
QIK  0.327  1.847  0.437  3.210  -0.176  1.984  0.113  9.91 
CFIK  0.307  0.767  0.170  0.628  0.198  3.654  0.357  3.56 
Note:  E:  number  of employees;  I:  gross  fixed  investment,  including  depreciation,  over 
beginning of period capital; QIK:  change in real sales over beginning of period capital; CFIK: 
real cash flow over beginning of period capital; Table 3: Dependent variable: IlK 
GMM IV estimates 
Poland  Czech Re£ublic  Bulgaria  Romania 
I  II  I  II  I  II  I  II 
QIK  0.020  0.038  0.018  0.010  0.040  0.016  0.012  0.007 
(6.932)  (6.753)  (1.610)  (2.027)  (1.767)  (2.099)  (1.783)  (2.273) 
CFIK  0.067  0.070  0.025  0.004 
(4.596)  (4.105)  (1.597)  (0.376) 
Number of  454  454  1044  1044  1182  1182  1730  1730 
firms 
Number of  1620  1620  4399  4399  5081  5081  7710  7710 
observations 
SarganTest  0.413  0.822  0.980  0.593  0.210  0.308  0.204  0.132 
SOC  1.476  1.487  1.053  0.991  1.277  1.267  1.741  1.634 
Note:  two  step  robust  t-statistics  in  brackets,  QIK  and  CFIK  are  instrumented  using  all 
available moment restrictions from t-2 and before. All equations include year dummies. The 
Sargan  test is  X2  distributed,  p-values are  reported,  p-values below 0.05  would  suggest a 
rejection of the validity of the instruments at  the 5%  critical level. The second order serial 
correlation test (SOC)  follows  a  Normal  distribution,  a  value  above  2  or below -2 would 
suggest presence of second order serial correlation at the 5% critical level.. Table 4: Dependent variable: 11K 
GMM IV estimates (only firms with positive investment rates) 
Poland  Czech Republic  Bul  aria  Romania 
I  II  I  II  I  II  I  II 
QIK  0.028  0.039  0.016  0.020  0.Q35  0.033  0.017  0.021 
(7.146)  (7.171)  (1.904)  (3.348)  (2.553)  (2.251)  (2.201)  (5.517) 
CFIK  0.054  0.058  0.031  0.007 
(5.411)  (11.05)  (1.792)  (1.048) 
Number of  422  422  901  901  1081  1081  1474  1474 
Firms 
Number of  856  856  2655  2655  2248  2248  3433  3433 
observations 
Sargan Test  0.467  0.247  0.401  0.433  0.467  0.179  0.232  0.263 
SOC  1.043  1.324  1.174  1.340  1.119  1.124  1.475  1.469 
Note:  as In table 3 

Defensive and strategic restructuring in Central European enterprises.&quot;

Finance and investment in transition: Czech enterprises, 1993-1994.&quot; IRES-Institut de Recherces,

Financing constraints and corporate investment.&quot;

Hardening the Budget Constraint: The Experience of Post-Socialist Countries&quot;,

Investment, credit rationing and the soft budget constraint: Evidence from Czech panel data.&quot;

Liquidity constraints and investment in transition economies: the case of Bulgaria.&quot;

Soft Budget Constraints and Transition&quot;

Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations.&quot;

The behavior of state firms in Eastern Europe pre-privatization.&quot;

The Economics of Post-Communist Transition,

The present value of profits and cyclical movement in investment.&quot;

Investment and credit constraints in transition economies : micro evidence from Poland, the Chrch Republic, Bulgaria and Romania.

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Investment and credit constraints in transition economies : micro evidence from Poland, the Chrch Republic, Bulgaria and Romania.

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